Noise and blur can reduce the quality of an image. Noise is variation in brightness or color of the pixels in an image and can be distributed according to a probability distribution (e.g., a uniform distribution, a Gaussian (normal) distribution, a Raleigh distribution, etc.). Noise can be concentrated in one or more color channels of an image or spread throughout an image. Examples of noise include graininess, color variation, and salt and pepper speckling.
Blur is a distortion of an image. An image can be blurred because, for example, the camera used to capture the image was out of focus, the camera moved while the image was being captured, the subject of the image moved while the image was being captured, the shutter-speed was too slow or too fast for the light conditions, or the lens of the camera was dirty.
Noise and blur are particular problems for images captured using mobile devices. The quality of mobile device cameras is often limited by size and weight constraints. The charge coupled devices used to capture the images on mobile devices often have bad low light capabilities and high noise characteristics. In addition, many mobile device cameras have a fixed focus, meaning that frequently, in order for the device to be close enough to capture necessary details in the image, the focal distance of the fixed focus lens will be violated, resulting in severe blur in the image.
FIG. 1 illustrates an example blurred image 102, and the de-blurred version of the image once an example prior art de-blur filter has been applied 104.
An image can be characterized as having a region of interest and additional visual information outside the region of interest. A region of interest is a region of the image that contains an object of interest, e.g., a barcode, a face, a license plate, or text. Other objects of interest are also possible.
For example, the image 102 contains a barcode 103 near the center of the image, but also has additional visual information such as the text 105 in the bottom right of the image and part of another barcode 107 in the upper right of the image. Assuming the object of interest is a complete barcode, the region of interest would be the region of the image containing a barcode 103. Everything else in the image is outside the region of interest.
Often an image is distorted because of noise as well as blur. The presence of noise in an image increases the likelihood of artifacts being introduced during de-blurring. The amount of de-blurring that can be done is limited by noise in the image. Noise introduces false edges in an image, and therefore artifacts, such as ringing, will form around noise pixels if the level of de-blur is high. Traditional de-blur filters may also enhance the noise in the image, resulting in an image such as 104 which has less blur than the original image 102, but also has additional noise. The noise includes black dots throughout the white portions of the image and white dots throughout the black portions of the image. The enhanced noise can make a de-blurred image difficult to decipher.